Infrared image super-resolution reconstruction by using generative adversarial network with an attention mechanism

Due to the limitations of infrared imaging principles and imaging systems, many problems are typically encountered with collected infrared images, such as low resolution, insufficient detail information, and blurred edges. In response to these problems, a method of infrared image super-resolution reconstruction that uses recursive attention and is based on a generative adversarial network is proposed. First, according to the characteristics of low-resolution infrared images such as uniform pixel distributions, low contrast, and poor perceived quality, a deep generator structure with a recursive-attention network is designed in this article. The recursive-attention module is used to extract high-frequency information from the feature maps, suppress useless information, and enhance the expressiveness of the features, which facilitates the reconstruction of texture details of infrared images. Then, to better distinguish the reconstructed images from the original high-resolution images, we designed a discriminator that was composed of a deep convolutional neural network. In addition, targeted improvements were made to the content loss function of GAN. We used the pre-trained VGG-19 network features before activation to calculate the perceptual loss, which helps recover the texture details of the infrared images. The experimental results on infrared image datasets demonstrated that the reconstruction performance of the proposed method is higher than those of several typical methods, and it realizes higher image visual quality.